short-paper

WBSum: Workload-based Summaries for RDF/S KBs

Authors:

Giannis Vassiliou,

Georgia Troullinou,

Nikolaos Papadakis,

Haridimos KondylakisAuthors Info & Claims

SSDBM '21: Proceedings of the 33rd International Conference on Scientific and Statistical Database Management

Pages 248 - 252

https://doi.org/10.1145/3468791.3468815

Published: 11 August 2021 Publication History

Abstract

Semantic summaries try to extract compact information from the original RDF graph, while reducing its size. State of the art structural semantic summaries, focus primarily on the graph structure of the data, trying to maximize the summary’s utility for a specific purpose, such as indexing, query answering and source selection. In this paper, we present an approach that is able to construct high quality summaries, exploiting a small part of the query workload, maximizing their utility for query answering, i.e. the query coverage. We demonstrate our approach using two real world datasets and the corresponding query workloads and we show that we strictly dominates current state of the art in terms of query coverage.

References

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Digital Library

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Cited By

Trouli GPapadakis NKondylakis H(2024)Constructing Semantic Summaries Using EmbeddingsInformation10.3390/info1504023815:4(238)Online publication date: 20-Apr-2024
https://doi.org/10.3390/info15040238
Vassiliou GPapadakis NKondylakis H(2023)SummaryGPT: Leveraging ChatGPT for Summarizing Knowledge GraphsThe Semantic Web: ESWC 2023 Satellite Events10.1007/978-3-031-43458-7_31(164-168)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-3-031-43458-7_31
Vassiliou GAlevizakis FPapadakis NKondylakis H(2023)iSummary: Workload-Based, Personalized Summaries for Knowledge GraphsThe Semantic Web10.1007/978-3-031-33455-9_12(192-208)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-33455-9_12
Show More Cited By

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Published In

cover image ACM Other conferences

SSDBM '21: Proceedings of the 33rd International Conference on Scientific and Statistical Database Management

July 2021

275 pages

ISBN:9781450384131

DOI:10.1145/3468791

Editors:
Qiang Zhu
University of Michigan - Dearborn, USA
,
Xingquan (Hill) Zhu
Florida Atlantic University, USA
,
Yicheng Tu
University of South Florida, USA
,
Zichen (Frank) Xu
Nanchang University, China
,
Anand Kumar
Amazon Inc., USA

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

Publication History

Published: 11 August 2021

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Refereed limited

Funding Sources

Hellenic Foundation for Research and Innovation (H.F.R.I.)

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SSDBM 2021

SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management

July 6 - 7, 2021

FL, Tampa, USA

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Overall Acceptance Rate 56 of 146 submissions, 38%

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Citations

Cited By

Trouli GPapadakis NKondylakis H(2024)Constructing Semantic Summaries Using EmbeddingsInformation10.3390/info1504023815:4(238)Online publication date: 20-Apr-2024
https://doi.org/10.3390/info15040238
Vassiliou GPapadakis NKondylakis H(2023)SummaryGPT: Leveraging ChatGPT for Summarizing Knowledge GraphsThe Semantic Web: ESWC 2023 Satellite Events10.1007/978-3-031-43458-7_31(164-168)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-3-031-43458-7_31
Vassiliou GAlevizakis FPapadakis NKondylakis H(2023)iSummary: Workload-Based, Personalized Summaries for Knowledge GraphsThe Semantic Web10.1007/978-3-031-33455-9_12(192-208)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-33455-9_12
Kellou-Menouer KKardoulakis NTroullinou GKedad ZPlexousakis DKondylakis H(2021)A survey on semantic schema discoveryThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00717-x31:4(675-710)Online publication date: 27-Nov-2021
https://dl.acm.org/doi/10.1007/s00778-021-00717-x

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